import pandas as pd
import seaborn as sns
import plotly.express as px
import matplotlib.pyplot as plt
import plotly.io as pio
pio.renderers.default = "plotly_mimetype+notebook"
For this excercise, we have written the following code to load the stock dataset built into plotly express.
stocks = px.data.stocks()
stocks.head()
| date | GOOG | AAPL | AMZN | FB | NFLX | MSFT | |
|---|---|---|---|---|---|---|---|
| 0 | 2018-01-01 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 1 | 2018-01-08 | 1.018172 | 1.011943 | 1.061881 | 0.959968 | 1.053526 | 1.015988 |
| 2 | 2018-01-15 | 1.032008 | 1.019771 | 1.053240 | 0.970243 | 1.049860 | 1.020524 |
| 3 | 2018-01-22 | 1.066783 | 0.980057 | 1.140676 | 1.016858 | 1.307681 | 1.066561 |
| 4 | 2018-01-29 | 1.008773 | 0.917143 | 1.163374 | 1.018357 | 1.273537 | 1.040708 |
Select a stock and create a suitable plot for it. Make sure the plot is readable with relevant information, such as date, values.
# YOUR CODE HERE
stocks.plot(x='date',y='MSFT')
plt.title('Microsoft Stock 2018-2019')
plt.ylabel('stock value')
plt.show()
You've already plot data from one stock. It is possible to plot multiples of them to support comparison.
To highlight different lines, customise line styles, markers, colors and include a legend to the plot.
stocks.plot(x='date', y=['GOOG','AAPL', 'AMZN','FB', 'NFLX', 'MSFT'])
plt.title('Stock 2018-2019')
plt.ylabel('stock value')
plt.show()
First, load the tips dataset
tips = sns.load_dataset('tips')
tips.head()
| total_bill | tip | sex | smoker | day | time | size | |
|---|---|---|---|---|---|---|---|
| 0 | 16.99 | 1.01 | Female | No | Sun | Dinner | 2 |
| 1 | 10.34 | 1.66 | Male | No | Sun | Dinner | 3 |
| 2 | 21.01 | 3.50 | Male | No | Sun | Dinner | 3 |
| 3 | 23.68 | 3.31 | Male | No | Sun | Dinner | 2 |
| 4 | 24.59 | 3.61 | Female | No | Sun | Dinner | 4 |
Let's explore this dataset. Pose a question and create a plot that support drawing answers for your question.
Some possible questions:
# Question: which group shows the tendency to give more tip? Smokers or Non-smokers?
g = sns.FacetGrid(tips, col='smoker', hue='sex')
g.map(sns.scatterplot, 'total_bill', 'tip')
plt.savefig('smoker.png', dpi=300)
plt.show()
# Answer: Non-smokers
Redo the above exercises (challenges 2 & 3) with plotly express. Create diagrams which you can interact with.
Hints:
# YOUR CODE HERE -> markers belum diganti based on line
df = px.data.stocks()
fig = px.line(df, x='date', y=['GOOG','AAPL', 'AMZN','FB', 'NFLX', 'MSFT'], markers=True, symbol='variable', title='Stocks 2018-2019')
fig.update_traces(marker_symbol = 6, selector = dict(type='tirangle-left'))
fig.show()
# YOUR CODE HERE
df = px.data.tips()
fig = px.scatter(df, x='total_bill', y='tip', title='Tip Given Trend Based on Total Bill Paid', color='sex', facet_col='smoker', facet_row='time')
fig.show()
Recreate the barplot below that shows the population of different continents for the year 2007.
Hints:
#load data
df = px.data.gapminder()
df.head()
| country | continent | year | lifeExp | pop | gdpPercap | iso_alpha | iso_num | |
|---|---|---|---|---|---|---|---|---|
| 0 | Afghanistan | Asia | 1952 | 28.801 | 8425333 | 779.445314 | AFG | 4 |
| 1 | Afghanistan | Asia | 1957 | 30.332 | 9240934 | 820.853030 | AFG | 4 |
| 2 | Afghanistan | Asia | 1962 | 31.997 | 10267083 | 853.100710 | AFG | 4 |
| 3 | Afghanistan | Asia | 1967 | 34.020 | 11537966 | 836.197138 | AFG | 4 |
| 4 | Afghanistan | Asia | 1972 | 36.088 | 13079460 | 739.981106 | AFG | 4 |
# Extract data
df = px.data.gapminder()
df_2007 = df.query('year==2007')
df_2007_new = df_2007.groupby('continent').sum()
# Use plotly bar
fig = px.bar(df_2007_new, x="pop", y=df_2007_new.index, orientation='h', color= df_2007_new.index,
text_auto='.2s', title='Population of the World in 2007 Based on Continents'
)
fig.update_yaxes(categoryorder='max ascending')
fig.show()